class_docstr: Creates random scatter plots as a testing utility.
  
  Data generation uses scikit-learn make_blobs to create scatter plots that
  have reasonable visual features and multiple colors.
  
  Parameters
  ----------
  ax : matplotlib Axes, default: None
      The axis to plot the figure on. If None is passed in the current axes
      will be used (or generated if required).
  
  n_samples : int, default: 100
      The number of points to generate for the scatter plot
  
  n_blobs : int or array of shape [n_centers, 2]
      Define the number of blobs to create or specify their centers.
  
  random_state : int, RandomState or None:
      Used to specify the seed of the random state to ensure tests work.
code: def draw(self, x, y, c):
      colors = resolve_colors(self.n_blobs)
  
      for i in np.arange(self.n_blobs):
          mask = c == i
          label = "c{}".format(i)
          self.ax.scatter(x[mask], y[mask], label=label, c=colors[i])
  
      return self.ax
  
file_path: yellowbrick/tests/rand.py
imports: import numpy
  from yellowbrick.base import Visualizer
  from yellowbrick.style import resolve_colors
  from sklearn.datasets import make_blobs
label_desc: Use image node_id 4 for calling the RandomVisualizer obj's underlying member method code with example usage: obj.draw(x, y, c) and returns: self